Survey Data Analysis (Quantitative)
Joe Ripberger
Introduction
Overview
This walkthrough covers the most common forms of survey data analysis:
- Descriptive Analysis - Summarizing key characteristics of variables
- Cross-Tabulation - Examining relationships between two variables
- Statistical Tests - T-tests for group comparisons
- Regression Analysis - Linear and logistic regression models
Special focus on proper handling of survey weights
Getting Started
Install and Load Packages
Key packages for survey data analysis:
tidyverse- Data wrangling and visualizationsurvey- Design-based survey analysissrvyr- Tidy interface for survey package
Import Survey Data
Import Survey Data
Rows: 1,354
Columns: 255
$ p_id <chr> "R_1myLufBiSmMh7hR", "R_3HH0M2zNhBG5Yid", "R_3m…
$ start_date <dttm> 2024-10-20 21:41:01, 2024-10-10 19:21:09, 2024…
$ end_date <dttm> 2024-10-20 21:52:14, 2024-10-10 20:15:26, 2024…
$ weight <dbl> 0.6575200, 2.7232804, 1.2694891, 0.9056132, 0.8…
$ age <dbl> 37, 19, 34, 58, 51, 69, 67, 33, 44, 30, 61, 31,…
$ gend <dbl> 1, 1, 0, 0, 0, 1, 1, 1, 0, 1, 0, 1, 1, 0, 0, 0,…
$ hisp <dbl> 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0,…
$ race <dbl> 6, 3, 1, 1, 1, 1, 1, 1, 1, 3, 1, 1, 2, 4, 1, 1,…
$ state_num <dbl> 33, 44, 42, 41, 14, 18, 3, 3, 5, 1, 1, 47, 31, …
$ zip <chr> "11779", "75204", "57052", "29414", "60137", "4…
$ long_years <dbl> 10, 2, 34, 21, 3, 40, 1, 6, 4, 9, 6, 30, 11, 4,…
$ last_state <dbl> NA, 44, NA, NA, 52, NA, 3, NA, 5, NA, NA, NA, N…
$ last_state_othr_spec <chr> NA, NA, NA, NA, "Ukraine", NA, NA, NA, NA, NA, …
$ last_zip <chr> NA, "75204", NA, NA, "39600", NA, "85085", NA, …
$ now <dbl> 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0,…
$ rural <dbl> 3, 1, 3, 2, 2, 3, 1, 2, 2, 3, 1, 2, 2, 2, 2, 2,…
$ home <dbl> 1, 3, 1, 1, 3, 1, 1, 1, 4, 3, 1, 2, 3, 1, 1, 1,…
$ home_spec <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
$ rent <dbl> 2, 1, 3, 3, 2, 3, 3, 3, 2, 2, 2, 3, 3, 1, 3, 3,…
$ adults <dbl> 4, 19, 2, 2, 4, 2, 2, 2, 1, 3, 2, 2, 10, 4, 2, …
$ children <dbl> 3, 0, 3, 0, 0, 0, 0, 1, 0, 0, 0, 1, 3, 1, 0, 0,…
$ follow <dbl> 5, 3, 4, 4, 5, 4, 4, 2, 2, 5, 5, 4, 5, 4, 5, 4,…
$ plan_around <dbl> 5, 3, 2, 2, 4, 3, 3, 3, 3, 5, 5, 4, 5, 4, 3, 2,…
$ und_weather <dbl> 5, 3, 3, 2, 1, 2, 2, 1, 3, 4, 5, 5, 5, 3, 2, 4,…
$ wthr_info_paper <dbl> 2, 1, 2, 1, 1, 2, 1, 1, 2, 3, 2, 3, 2, 1, 2, 1,…
$ wthr_info_web <dbl> 2, 4, 2, 1, 1, 5, 2, 5, 5, 4, 6, 3, 5, 4, 3, 1,…
$ wthr_info_govweb <dbl> 2, 4, 2, 4, 1, 2, 4, 1, 6, 3, 6, 3, 4, 4, 2, 1,…
$ wthr_info_loctv <dbl> 2, 5, 2, 2, 1, 4, 2, 1, 4, 6, 6, 4, 4, 3, 6, 5,…
$ wthr_info_cabtv <dbl> 2, 5, 1, 2, 1, 2, 2, 1, 4, 4, 6, 3, 4, 2, 4, 3,…
$ wthr_info_radio <dbl> 2, 3, 1, 1, 1, 3, 2, 1, 3, 6, 2, 1, 4, 1, 3, 1,…
$ wthr_info_fam <dbl> 2, 2, 5, 1, 5, 3, 3, 1, 3, 6, 6, 3, 4, 3, 3, 2,…
$ wthr_info_soc <dbl> 3, 3, 3, 1, 5, 2, 1, 1, 4, 4, 6, 5, 5, 1, 2, 1,…
$ wthr_info_phone <dbl> 2, 3, 4, 5, 6, 5, 1, 5, 2, 4, 6, 3, 4, 2, 3, 5,…
$ risk_hail <dbl> 4, 3, 3, 2, 3, 3, 2, 2, NA, 2, 4, 3, 4, 3, 4, 3…
$ risk_wind <dbl> 4, 2, 3, 4, 3, 3, 2, 3, 3, 2, 5, 4, 4, 3, 4, 3,…
$ risk_lignt <dbl> 4, 3, 3, 5, 3, 3, 2, 3, 5, 3, 5, 4, 4, 3, 4, 3,…
$ risk_heat <dbl> 4, 2, 3, 5, 2, 3, 3, 5, 4, 5, 5, 3, 5, 3, 4, 3,…
$ risk_rain <dbl> 4, 2, 3, 3, 2, 2, 1, 2, NA, 4, 5, 5, 4, 3, 2, 2…
$ risk_drought <dbl> 4, 3, 3, 3, 2, 3, 3, 5, NA, 4, 1, 4, 4, 3, 4, 2…
$ risk_cold <dbl> 4, 2, 2, 1, 3, 2, 1, 1, NA, 5, 4, 4, 5, 3, 3, 4…
$ risk_snow <dbl> 4, 1, 3, 2, 4, 2, 1, 1, NA, 2, 2, 4, 5, 1, 4, 4…
$ risk_ice <dbl> 4, 3, 3, 2, 3, 3, 1, 1, NA, 3, 2, 2, 5, 3, 4, 4…
$ risk_tor <dbl> 4, 4, 3, 4, 2, 4, 2, 2, NA, 3, 5, 3, 4, 2, 3, 2…
$ risk_flood <dbl> 4, 4, 3, 3, 2, 2, 2, 4, 3, 1, 5, 3, 4, 2, 2, 1,…
$ risk_hur <dbl> 4, 3, 1, 4, 2, 2, 1, 1, 2, 2, 5, 3, 4, 1, 2, 1,…
$ risk_fire <dbl> 4, 3, 2, 2, 1, 3, 2, 5, NA, 2, 1, 4, 4, 3, 4, 1…
$ risk_surge <dbl> 4, 3, 1, 3, 3, 1, 1, 2, 1, 3, 3, 2, 5, 3, 2, 3,…
$ risk_tie <dbl> 1, 8, 11, 4, 2, 10, 4, 6, 6, 6, 10, 11, 7, 2, 8…
$ alert_und <dbl> 5, 3, 4, 5, 5, 4, 4, 2, 3, 5, 5, 4, 5, 3, 5, 4,…
$ torwatch <dbl> NA, 2, NA, 2, 2, NA, NA, 1, 2, NA, NA, NA, NA, …
$ torwarn <dbl> NA, NA, 2, NA, NA, 2, 2, NA, NA, 1, 2, 1, 2, NA…
$ warn_time <dbl> 2, 2, 3, 1, 1, 1, 1, 3, 3, 1, 1, 2, 4, 3, 1, 1,…
$ warn_size <dbl> 2, 2, 3, 2, 2, 3, NA, 2, 3, 5, 2, 2, 2, 2, 2, 2…
$ warn_time_minutes <dbl> NA, NA, NA, 4, 12, 55, 60, NA, NA, 3, 2, NA, NA…
$ warn_time_hours <dbl> 7, 8, NA, NA, NA, NA, NA, NA, NA, NA, NA, 6, NA…
$ watch_time <dbl> 2, 3, 2, 1, 1, 2, 2, 4, 4, 1, 1, 2, 4, 4, 2, 2,…
$ watch_size <dbl> 2, 2, 3, 3, 2, 4, NA, 3, 3, 5, 2, 2, 4, 2, 3, 3…
$ watch_time_minutes <dbl> NA, NA, NA, 61, 13, NA, NA, NA, NA, 5, 11, NA, …
$ watch_time_hours <dbl> 5, NA, 6, NA, NA, 12, 8, NA, NA, NA, NA, 24, NA…
$ tor_watchwarn_und <dbl> 5, 1, 3, 4, 5, 3, 2, 1, 5, 4, 5, 3, 5, 2, 2, 3,…
$ tor_map_und <dbl> 5, 3, 2, 4, 4, 3, 2, 1, 5, 3, 5, 3, 4, 3, 2, 4,…
$ tor_radar_und <dbl> 5, 2, 2, 4, 3, 3, 2, 1, 5, 5, 5, 3, 5, 2, 2, 4,…
$ svr_hail <dbl> 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 1, 1,…
$ svr_wind <dbl> 0, 1, 0, 0, 1, 1, 1, 0, 0, 0, 1, 0, 0, 1, 1, 1,…
$ svr_lightning <dbl> 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 1, 0, 0, 1, 1, 1,…
$ svr_flood <dbl> 1, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 1, 1, 1,…
$ svr_rain <dbl> 0, 0, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1,…
$ svr_watchwarn_und <dbl> 5, 3, 3, 3, 5, 2, 2, 2, 3, 4, 5, 3, 5, 2, 2, 3,…
$ ffd_und <dbl> 5, 3, 3, 4, 4, 2, 2, 4, 5, 5, 5, 3, 5, 2, 2, 2,…
$ ffd_watchwarn_und <dbl> 5, 4, 3, 5, 5, 4, 4, 5, 5, 5, 5, 4, 5, 3, 4, 2,…
$ rec_all <dbl> 5, 2, 3, 5, 5, 3, 3, 4, 4, 5, 5, 4, 5, 3, 4, 3,…
$ rec_most <dbl> 5, 2, 3, 5, 5, 4, 3, 4, 2, 5, 5, 5, 5, 4, 4, 3,…
$ rec_soon <dbl> 5, 3, 3, 5, 5, 4, 3, 4, 2, 5, 5, 4, 5, 2, 3, 3,…
$ rec_miss <dbl> 5, 2, 3, 2, 1, 3, 4, 2, 2, 1, 1, 5, 5, 3, 3, 4,…
$ rec_area <dbl> 5, 3, 3, 2, 1, 3, 2, 2, 5, 2, 4, 4, 5, 3, 3, 4,…
$ rec_time <dbl> 5, 3, 3, 4, 5, 3, 4, 4, 5, 2, 1, 4, 5, 4, 4, 4,…
$ rec_sleep <dbl> 5, 3, 3, 5, 5, 3, 3, 4, 5, 2, 5, 4, 5, 3, 2, 1,…
$ rec_driving <dbl> 5, 4, 3, 1, 5, 3, 4, 4, 5, 5, 5, 4, 5, 3, 2, 1,…
$ rec_work <dbl> 5, 3, 3, 4, 5, 4, 4, 4, 5, 5, 5, 5, 5, 3, 2, 5,…
$ rec_store <dbl> 5, 3, 4, 2, 5, 4, 3, 4, 5, 4, 5, 4, 5, 3, 2, 3,…
$ rec_small_group <dbl> 5, 3, 4, 2, 5, 3, 3, 4, 5, 5, 5, 5, 5, 3, 2, 3,…
$ rec_large_group <dbl> 5, 3, 3, 2, 5, 3, 3, 4, 5, 5, 5, 5, 5, 3, 2, 4,…
$ rec_stream <dbl> 5, 3, 3, 1, 5, 3, 3, 4, 5, 2, 5, 4, 5, 3, 2, 1,…
$ rec_phone <dbl> 5, 3, 3, 2, 1, 2, 3, 1, 5, 4, 1, 4, 5, 3, 1, 1,…
$ rec_diff_sit <chr> "No", NA, NA, NA, "Only when my phone is off", …
$ warn_hist <dbl> 1, 0, 0, 1, 1, 1, 0, 1, NA, 1, 1, 0, 0, 0, 0, 1…
$ warn_when <dbl> 2, NA, NA, 1, 2, 3, NA, 4, NA, 2, 1, NA, NA, NA…
$ warn_how_br_rad <dbl> 1, NA, NA, NA, 0, 0, NA, 0, NA, 1, 0, NA, NA, N…
$ warn_how_wx_rad <dbl> 0, NA, NA, NA, 0, 1, NA, 0, NA, 1, 0, NA, NA, N…
$ warn_how_tv <dbl> 0, NA, NA, NA, 0, 1, NA, 0, NA, 1, 0, NA, NA, N…
$ warn_how_siren <dbl> 0, NA, NA, NA, 1, 0, NA, 0, NA, 1, 0, NA, NA, N…
$ warn_how_int <dbl> 0, NA, NA, NA, 0, 0, NA, 0, NA, 1, 0, NA, NA, N…
$ warn_how_soc <dbl> 0, NA, NA, NA, 0, 0, NA, 0, NA, 0, 0, NA, NA, N…
$ warn_how_word <dbl> 0, NA, NA, NA, 0, 1, NA, 0, NA, 1, 0, NA, NA, N…
$ warn_how_phone <dbl> 0, NA, NA, NA, 1, 1, NA, 1, NA, 1, 1, NA, NA, N…
$ warn_how_oth <dbl> 0, NA, NA, NA, 0, 0, NA, 0, NA, 0, 0, NA, NA, N…
$ warn_how_dk <dbl> 0, NA, NA, NA, 0, 0, NA, 0, NA, 0, 0, NA, NA, N…
$ warn_how_spec <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
$ warn_timercv <dbl> 2, NA, NA, 3, 3, 2, NA, 4, NA, 3, 4, NA, NA, NA…
$ warn_where <dbl> 1, NA, NA, 1, 1, 6, NA, 1, NA, 1, 1, NA, NA, NA…
$ warn_where_spec <chr> NA, NA, NA, NA, NA, "Vacation", NA, NA, NA, NA,…
$ warn_iss <dbl> 1, NA, NA, 1, 1, 1, NA, 1, NA, 1, 1, NA, NA, NA…
$ warn_sure <dbl> 1, NA, NA, 1, 1, 1, NA, 1, NA, 1, 1, NA, NA, NA…
$ warn_tor <dbl> 1, NA, NA, 0, 1, 1, NA, 0, NA, 1, 1, NA, NA, NA…
$ last_act <dbl> 0, NA, NA, 2, 1, NA, NA, 0, NA, 5, 2, NA, NA, N…
$ last_act_spec <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
$ last_act_satis <dbl> 5, NA, NA, 4, 4, NA, NA, 1, NA, 4, 5, NA, NA, N…
$ last_act_again <dbl> 5, NA, NA, 2, 5, NA, NA, 1, NA, 5, 5, NA, NA, N…
$ next_act_day <dbl> 1, 4, 3, 2, 1, 2, 2, 1, 4, 1, 2, 4, 4, 2, 2, 1,…
$ next_act_day_spec <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
$ next_act_night <dbl> 0, 3, 3, 2, 1, 3, 2, 2, 4, 5, 2, 2, 1, 2, 2, 1,…
$ next_act_night_spec <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
$ resp_ignore <dbl> 5, 3, 3, 1, 2, 4, 2, 3, 2, 1, 1, 4, 5, 2, 2, 2,…
$ resp_prot <dbl> 5, 3, 3, 4, 3, 3, 4, 2, 3, 5, 5, 4, 5, 3, 4, 3,…
$ resp_busy <dbl> 5, 3, 3, 1, 4, 3, 2, 3, 4, 1, 1, 5, 5, 3, 2, 4,…
$ resp_unsure <dbl> 5, 3, 3, 1, 4, 2, 2, 4, 2, 2, 1, 4, 5, 3, 2, 1,…
$ resp_sleep <dbl> 5, 3, 3, 4, 3, 2, 3, 3, 2, 5, 5, 5, 5, 3, 4, 2,…
$ resp_driving <dbl> 5, 3, 3, 3, 2, 2, 4, 4, 1, 4, 5, 5, NA, 3, 3, 2…
$ resp_work <dbl> 5, 3, 3, 4, 3, 4, 4, 4, 3, 5, 5, 5, 5, 2, 4, 5,…
$ resp_store <dbl> 5, 3, 3, 4, 3, 4, 4, 4, 4, 4, 5, 4, 5, 2, 3, 5,…
$ resp_small_group <dbl> 5, 4, 3, 3, 3, 3, 4, 4, 4, 5, 5, 4, 5, 3, 3, 3,…
$ resp_large_group <dbl> 5, 4, 3, 3, 3, 3, 4, 4, 3, 5, 5, 5, 5, 3, 4, 2,…
$ resp_stream <dbl> 5, 4, 3, 4, 3, 2, 4, 4, 3, 2, 5, 4, 5, 3, 4, 2,…
$ resp_phone <dbl> 5, 2, 3, 2, 1, 2, 3, 1, 2, 2, 5, 4, 5, 2, 3, 3,…
$ resp_dif_sit <chr> "No", NA, NA, NA, NA, NA, NA, NA, "No", "Phone …
$ rec_morn <dbl> 5, NA, 4, 5, 4, 3, 3, 4, 3, 5, 5, 3, 5, 3, 3, 4…
$ und_morn <dbl> 5, 3, 4, 5, 4, 3, 3, 4, 2, 4, 5, 3, 5, 3, 4, 4,…
$ resp_morn <dbl> 5, 4, 4, 5, 3, 3, 4, 3, 2, 5, 5, 4, 5, 3, 4, 3,…
$ rec_aft <dbl> 5, 5, 4, 5, 4, 3, 3, 4, 5, 5, 5, 3, 5, 3, 3, 5,…
$ und_aft <dbl> 5, 3, 4, 5, 4, 3, 3, 4, 5, 4, 5, 3, 5, 3, 3, 3,…
$ resp_aft <dbl> 5, 3, 4, 5, 3, 3, 4, 4, 5, 4, 5, 4, 5, 3, 4, 3,…
$ rec_eve <dbl> 5, 3, 4, 5, 4, 3, 3, 4, 5, 4, 5, 4, 5, 3, 3, 1,…
$ und_eve <dbl> 5, 3, 4, 5, 4, 3, 3, 4, 3, 4, 5, 5, 5, 3, 3, 3,…
$ resp_eve <dbl> 5, 3, 4, 5, 3, 3, 4, 4, 2, 5, 5, 4, 5, 3, 4, 2,…
$ tor_eff1 <dbl> 5, 3, 3, 1, 1, 4, 2, 2, 3, 5, 5, 5, 5, 2, 4, 4,…
$ tor_eff2 <dbl> 5, 3, 3, 5, 2, 4, 3, 4, 2, 5, 5, 3, 5, 3, 4, 4,…
$ tor_eff3 <dbl> 5, 2, 3, 3, 2, 3, 3, 4, 2, 5, 5, 4, 5, 3, 4, 4,…
$ tor_eff4 <dbl> 5, 4, 4, 1, 4, 4, 3, 2, 3, 4, 5, 4, 5, 4, 4, 4,…
$ tor_eff5 <dbl> 5, 3, 3, 1, 1, 4, 2, 2, 3, 5, 5, 4, 5, 3, 4, 4,…
$ tor_eff6 <dbl> 5, 3, 3, 5, 2, 4, 4, 4, 3, 5, 5, 5, 5, 2, 3, 4,…
$ tor_eff7 <dbl> 5, 4, 3, 3, 2, 4, 4, 4, 4, 4, 5, 4, 5, 3, 4, 4,…
$ tor_eff8 <dbl> 5, 2, 4, 1, 4, 4, 4, 2, 4, 5, 5, 4, 5, 4, 4, 4,…
$ nws_trust <dbl> 5, 3, 3, 4, 5, 4, 2, 4, 4, 5, 5, 4, 5, 3, 3, 4,…
$ lotv_trust <dbl> 5, 3, 3, 5, 4, 4, 2, 3, 3, 3, 5, 5, 4, 3, 3, 4,…
$ natv_trust <dbl> 5, 4, 3, 4, 4, 4, 1, 4, 4, 5, 5, 4, 4, 3, 3, 4,…
$ em_trust <dbl> 5, 5, 3, 4, 4, 4, 2, 4, 3, 5, 5, 3, 5, 3, 3, 4,…
$ fam_trust <dbl> 5, 2, 3, 2, 4, 3, 3, 2, 5, 2, 5, 4, 4, 2, 3, 2,…
$ wx_info1 <dbl> 5, 3, 3, 4, 4, 3, 2, 1, 4, 5, 4, 3, 5, 1, 3, 1,…
$ wx_info2 <dbl> 5, 3, 3, 4, 4, 4, 3, 1, 3, 5, 5, 3, 5, 2, 3, 3,…
$ wx_info3 <dbl> 5, 3, 3, 4, 4, 3, 2, 3, 3, 5, 5, 3, 5, 2, 5, 5,…
$ wx_info4 <dbl> 5, 4, 3, 3, 5, 4, 2, 3, 2, 4, 3, 4, 5, 3, 3, 3,…
$ wx_info5 <dbl> 5, 2, 3, 5, 3, 3, 2, 3, 3, 2, 2, 3, 5, 3, 1, 1,…
$ wx_info6 <dbl> 5, 3, 3, 3, 3, 3, 3, 2, 2, 1, 3, 2, 5, 3, 3, 2,…
$ wx_info7 <dbl> 5, 3, 3, 4, 5, 4, 2, 5, 3, 5, 5, 3, 5, 3, 4, 1,…
$ wx_info8 <dbl> 5, 4, 3, 1, 5, 3, 3, 1, 4, 5, 5, 4, 5, 2, 1, 2,…
$ wx_info_tie <dbl> 8, 3, 7, 4, 8, 3, 2, 7, 4, 2, 7, 3, 3, 4, 3, 3,…
$ rand_svr_con <dbl> 5, 3, 3, 2, 4, 3, 3, 1, 5, 5, 5, 3, 5, 4, 4, 1,…
$ rand_svr_resp <dbl> 5, 2, 3, 2, 3, 3, 3, 1, 5, 5, 5, 3, 5, 3, 4, 1,…
$ rand_svr_und <dbl> 5, 3, 3, 5, 5, 3, 4, 3, 2, 5, 5, 4, 5, 4, 2, 1,…
$ rand_svr_conf <dbl> 5, 3, 3, 4, 2, 3, 3, 4, 1, 4, 5, 3, 5, 3, 3, 1,…
$ rand_otlk_cern <dbl> 5, 3, 3, 4, 5, 3, 4, 3, 2, 5, 1, 3, 5, 5, 3, 3,…
$ rand_otlk_point_risk <dbl> 7, NA, NA, 7, NA, NA, 7, 6, NA, 4, 7, 5, NA, NA…
$ rand_otlk_point_plan <dbl> 5, NA, NA, 3, NA, NA, 5, 4, NA, 4, 5, 4, NA, NA…
$ rand_otlk_drive_risk <dbl> NA, 5, 4, NA, 6, 7, NA, NA, 3, NA, NA, NA, 5, 7…
$ rand_otlk_drive_plan <dbl> NA, 2, 3, NA, 5, 2, NA, NA, 4, NA, NA, NA, 5, 5…
$ rand_otlk_und <dbl> 5, 3, 3, 5, 4, 3, 4, 4, 2, 2, 5, 4, 5, 5, 2, 3,…
$ rand_otlk_help <dbl> 5, 4, 3, 5, 5, 4, 4, 4, 3, 5, 5, 3, 5, 5, 2, 3,…
$ rand_otlk_use <dbl> 5, 2, 3, 5, 5, 3, 5, 4, 3, 5, 5, 2, 5, 5, 2, 3,…
$ exf_aware <dbl> 1, 23, 23, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, …
$ exf_access <dbl> 5, NA, NA, 4, 1, 3, 3, 4, 1, 5, 4, NA, 3, 2, 3,…
$ exf_info_loctv <dbl> 0, NA, NA, 0, NA, 1, 0, 0, NA, 1, 1, NA, 0, NA,…
$ exf_info_web <dbl> 0, NA, NA, 0, NA, 1, 1, 0, NA, 0, 1, NA, 0, NA,…
$ exf_info_phone <dbl> 0, NA, NA, 1, NA, 1, 0, 1, NA, 1, 1, NA, 0, NA,…
$ exf_info_govweb <dbl> 1, NA, NA, 0, NA, 0, 1, 0, NA, 0, 1, NA, 0, NA,…
$ exf_info_cabtv <dbl> 0, NA, NA, 0, NA, 0, 0, 0, NA, 1, 1, NA, 1, NA,…
$ exf_info_soc <dbl> 0, NA, NA, 1, NA, 0, 0, 0, NA, 0, 0, NA, 0, NA,…
$ exf_info_fam <dbl> 0, NA, NA, 0, NA, 0, 0, 0, NA, 0, 0, NA, 0, NA,…
$ exf_info_radio <dbl> 0, NA, NA, 0, NA, 1, 0, 0, NA, 1, 0, NA, 0, NA,…
$ exf_info_paper <dbl> 0, NA, NA, 0, NA, 0, 0, 0, NA, 0, 0, NA, 0, NA,…
$ exf_use <chr> "Yes", NA, NA, "Planning outdoor activities", N…
$ exf_trust <dbl> 5, NA, NA, 3, NA, 3, 3, 3, NA, 5, 5, NA, 5, NA,…
$ exf_reliable <dbl> 5, NA, NA, 3, NA, 4, 3, 2, NA, 5, 5, NA, 5, NA,…
$ exf_detail <dbl> 5, NA, NA, 2, NA, 4, 2, 3, NA, 5, 5, NA, 5, NA,…
$ exf_access_no_inacc <dbl> NA, NA, NA, NA, 1, NA, NA, NA, 0, NA, NA, NA, N…
$ exf_access_no_rel <dbl> NA, NA, NA, NA, 0, NA, NA, NA, 0, NA, NA, NA, N…
$ exf_access_no_fnd <dbl> NA, NA, NA, NA, 0, NA, NA, NA, 1, NA, NA, NA, N…
$ exf_access_no_int <dbl> NA, NA, NA, NA, 0, NA, NA, NA, 0, NA, NA, NA, N…
$ exf_access_no_oth <dbl> NA, NA, NA, NA, 0, NA, NA, NA, 0, NA, NA, NA, N…
$ exf_access_no_oth_spec <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
$ exf_ex_see <dbl> 0, 0, 2, 2, 0, 0, 0, 0, 1, 1, 1, 0, 1, 1, 0, 0,…
$ exf_ex_risk <dbl> 5, 3, 2, 2, 4, 1, 3, 2, 2, 3, 3, 3, 5, 2, 2, 1,…
$ exf_resp <dbl> 4, 2, 1, 1, 3, 0, 1, 1, 1, 1, 2, 2, 4, 1, 1, 0,…
$ exf_int_obj <dbl> 5, 3, 2, 2, 3, 1, 3, 3, 2, 2, 2, 3, 5, 2, 3, 3,…
$ exf_ex_monitor3 <dbl> NA, 3, 3, 4, NA, NA, NA, NA, 3, NA, NA, NA, 5, …
$ exf_ex_monitor5 <dbl> NA, NA, NA, NA, 5, 3, NA, NA, NA, 5, NA, 3, NA,…
$ exf_ex_monitor7 <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
$ exf_ex_conf <dbl> 5, 3, 4, 5, 4, 3, 3, 3, 3, 5, 5, 5, 5, 5, 2, 3,…
$ exf_ex_use <dbl> 5, 3, 3, 4, 4, 4, 3, 4, 2, 5, 4, 4, 2, 5, 3, 2,…
$ exf_int_no_conf <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
$ exf_use_no_rel <dbl> NA, NA, NA, NA, NA, NA, NA, NA, 0, NA, NA, NA, …
$ exf_use_no_inacc <dbl> NA, NA, NA, NA, NA, NA, NA, NA, 0, NA, NA, NA, …
$ exf_use_no_fnd <dbl> NA, NA, NA, NA, NA, NA, NA, NA, 1, NA, NA, NA, …
$ exf_use_no_int <dbl> NA, NA, NA, NA, NA, NA, NA, NA, 0, NA, NA, NA, …
$ exf_use_no_oth <dbl> NA, NA, NA, NA, NA, NA, NA, NA, 0, NA, NA, NA, …
$ exf_use_no_oth_spec <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
$ timing_aware <dbl> 1, 2, 2, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0,…
$ timing_see <dbl> 1, 1, 1, 0, 0, 2, 0, 0, 2, 0, 1, 0, 1, 0, 0, 0,…
$ timing_int_sub <dbl> 5, 3, 3, 5, 4, 3, 2, 1, 4, 3, 4, 3, 5, 4, 2, 3,…
$ timing_start_obj <dbl> 6, NA, 11, 32, 30, 32, 32, 31, 12, 27, 27, 9, 1…
$ timing_like_obj <dbl> 11, NA, 12, 37, 36, 35, 37, NA, 14, 7, 31, 13, …
$ timing_end_obj <dbl> 12, NA, 13, 3, 44, 37, 44, 36, 17, 9, 3, 23, 11…
$ timing_use <dbl> 5, 4, 3, 4, 4, 4, 4, 4, 1, 5, 4, 3, 4, 4, 3, 1,…
$ timing_into_no_why <chr> NA, NA, NA, NA, NA, NA, "No", "Top right corner…
$ timing_use_no_fnd <dbl> NA, NA, NA, NA, NA, NA, NA, NA, 0, NA, NA, NA, …
$ timing_use_no_info <dbl> NA, NA, NA, NA, NA, NA, NA, NA, 0, NA, NA, NA, …
$ timing_use_no_inacc <dbl> NA, NA, NA, NA, NA, NA, NA, NA, 1, NA, NA, NA, …
$ timing_use_no_rel <dbl> NA, NA, NA, NA, NA, NA, NA, NA, 1, NA, NA, NA, …
$ timing_use_no_int <dbl> NA, NA, NA, NA, NA, NA, NA, NA, 0, NA, NA, NA, …
$ timing_use_no_oth <dbl> NA, NA, NA, NA, NA, NA, NA, NA, 0, NA, NA, NA, …
$ timing_use_no_oth_spec <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
$ your_ability <dbl> 5, 2, 3, 3, 4, 3, 2, 4, 3, 5, 5, 3, 5, 5, 2, 2,…
$ rq_1 <dbl> 1, 2, 2, 1, 2, 2, 2, 2, 1, 1, 1, 0, 1, 2, 2, 2,…
$ rq_2 <dbl> 1, NA, 2, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 0…
$ rq_3 <dbl> 1, 1, 2, 1, 1, 0, 0, 0, 1, 1, 2, 0, 1, 0, 1, 1,…
$ rq_4 <dbl> 1, 2, 2, 0, 0, 1, 1, 0, 1, 1, 1, 0, 1, 0, 0, 0,…
$ rq_5 <dbl> 1, 0, 2, 0, 1, 1, 1, 0, 2, 1, 1, 1, 1, 0, 0, 0,…
$ rq_6 <dbl> 1, 1, 2, 0, 0, 0, 1, 0, 0, 1, 1, 0, 1, 0, 0, 0,…
$ rq_7 <dbl> 1, 1, 2, 0, 0, 0, 1, 0, 0, 1, 1, 0, 1, 0, 0, 0,…
$ rq_8 <dbl> 1, 0, 2, 0, 0, 1, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0,…
$ rq_9 <dbl> 1, 1, 2, 0, 0, 1, 0, 0, 1, 1, 2, 1, 1, 0, 0, 0,…
$ rq_10 <dbl> 1, 2, 2, 1, 0, 0, 1, 1, 1, 1, 0, 0, 1, 0, 0, 1,…
$ income <dbl> 4, 2, 2, 3, 3, 2, 4, 2, 3, 1, 1, 2, 3, 3, 3, 2,…
$ inc_50 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, 1, 2, NA, N…
$ inc_100 <dbl> NA, 6, 8, NA, NA, 10, NA, 7, NA, NA, NA, 7, NA,…
$ inc_150 <dbl> NA, NA, NA, 11, 11, NA, NA, NA, 12, NA, NA, NA,…
$ inc_200 <dbl> 17, NA, NA, NA, NA, NA, 17, NA, NA, NA, NA, NA,…
$ edu <dbl> 6, 2, 2, 7, 6, 4, 3, 2, 2, 5, 4, 6, 2, 4, 4, 5,…
$ comments <chr> NA, "Nothing else", "Nothing comes to mind", NA…
$ watch_warn <dbl> 1, 1, 2, 1, 1, 2, 2, 1, 1, 2, 2, 2, 2, 1, 1, 1,…
$ rand_morn <time> 08:00:00, 03:00:00, 03:00:00, 08:00:00, 09:00:…
$ rand_aft <time> 16:00:00, 15:00:00, 13:00:00, 15:00:00, 16:00:…
$ rand_eve <time> 22:00:00, 21:00:00, 23:00:00, 21:00:00, 22:00:…
$ exf_ex_loc <chr> "Location B", "Location A", "Location B", "Loca…
$ exf_ex_day <chr> "7 days (next Saturday)", "3 days (Tuesday)", "…
$ rand_otlk_loc <chr> "memphis", "indy_to_okc", "indy_to_okc", "st_lo…
$ rand_otlk_format <chr> "static", "static", "interactive", "interactive…
$ rand_svr <chr> "wind", "wind", "wind_hail", "wind", "severe", …
$ state <chr> "New York", "Texas", "South Dakota", "South Car…
$ time_taken <dbl> 11.216667, 54.283333, 17.650000, 41.883333, 32.…
$ age_group <dbl> 3, 1, 2, 5, 4, 6, 6, 2, 3, 2, 5, 2, 1, 1, 6, 6,…
$ nws_region <chr> "Eastern Region", "Southern Region", "Central R…
$ exf_ex_monitor <dbl> NA, 3, 3, 4, 5, 3, NA, NA, 3, 5, NA, 3, 5, NA, …
$ censusproportion <dbl> 0.000229, 0.000168, 0.029448, 0.028490, 0.02879…
$ surveyproportion <dbl> 0.004566210, 0.003154574, 0.025723473, 0.034246…
$ weightfactor <dbl> 0.050151, 0.053256, 1.144791, 0.831908, 0.99516…Define Survey Design Object
Two approaches to create survey design objects:
# Option 1: Using srvyr package (tidyverse-friendly)
survey_tidy <- survey_raw |>
as_survey_design(
ids = 1, # No clustering in the sample design
weights = weightfactor # Weight variable
)
# Option 2: Using survey package (traditional approach)
survey_base <- svydesign(
ids = ~1, # No clustering
weights = ~weightfactor, # Weight variable
data = survey_raw
)survey_tidyworks with dplyr-style functionssurvey_baserequired for statistical tests
Descriptive Analysis
Frequencies and Percentages
Summarize categorical variables with and without weights:
Frequencies and Percentages
# A tibble: 5 × 3
follow n percentage
<dbl> <int> <dbl>
1 1 29 2.15
2 2 119 8.81
3 3 253 18.7
4 4 653 48.4
5 5 296 21.9 Frequencies and Percentages
With weights (using srvyr):
Frequencies and Percentages
# A tibble: 5 × 6
follow n n_se percentage percentage_low percentage_upp
<dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 1 23.0 5.34 2.05 1.30 3.23
2 2 114. 13.3 10.2 8.15 12.7
3 3 213. 15.4 19.0 16.5 21.8
4 4 515. 21.8 46.0 42.7 49.5
5 5 254. 18.4 22.7 19.9 25.8 Frequencies and Percentages
Using the survey package:
Frequencies and Percentages
follow
1 2 3 4 5
2.052472 10.187442 19.003746 46.043782 22.712558 Visualizing Frequencies
Bar chart with confidence intervals:
survey_tidy |>
drop_na(follow) |>
group_by(follow) |>
summarise(percentage = survey_prop(vartype = "ci") * 100) |>
ggplot(aes(x = follow, y = percentage)) +
geom_col(fill = "steelblue") +
geom_errorbar(aes(ymin = percentage_low, ymax = percentage_upp),
width = 0.2) +
scale_x_continuous(breaks = 1:5,
labels = c("Strongly disagree",
"Disagree",
"Neither disagree\nnor agree",
"Agree",
"Strongly agree")) +
labs(title = "How much do you agree or disagree with the following statements?",
subtitle = "I follow the weather very closely.",
x = "Response Category",
y = "Estimated Percent of Respondents (95% CI)") +
theme_classic()Visualizing Frequencies
Means, Medians, and Standard Deviations
Summarize continuous variables:
Means, Medians, and Standard Deviations
# A tibble: 1 × 3
mean_value median_value sd_value
<dbl> <dbl> <dbl>
1 51.4 52 16.4Means, Medians, and Standard Deviations
With weights (using srvyr):
Means, Medians, and Standard Deviations
# A tibble: 1 × 7
mean_value mean_value_low mean_value_upp median_value median_value_low
<dbl> <dbl> <dbl> <dbl> <dbl>
1 48.7 47.3 50.0 48 46
# ℹ 2 more variables: median_value_upp <dbl>, sd_value <dbl>Means, Medians, and Standard Deviations
Using the survey package:
Means, Medians, and Standard Deviations
mean SE
age 48.66 0.6911$age
quantile ci.2.5 ci.97.5 se
0.5 48 46 51 1.274393
attr(,"hasci")
[1] TRUE
attr(,"class")
[1] "newsvyquantile"Visualizing Continuous Variables
Weighted histogram:
Visualizing Continuous Variables
Cross-Tabulation
Two Categorical Variables
Examine relationships between categorical variables:
Two Categorical Variables
# A tibble: 10 × 4
# Groups: gend [2]
gend follow n percentage
<dbl> <dbl> <int> <dbl>
1 0 1 9 1.38
2 0 2 58 8.92
3 0 3 129 19.8
4 0 4 319 49.1
5 0 5 135 20.8
6 1 1 20 2.86
7 1 2 61 8.71
8 1 3 124 17.7
9 1 4 334 47.7
10 1 5 161 23 Two Categorical Variables
With weights (using srvyr):
Two Categorical Variables
# A tibble: 10 × 7
# Groups: gend [2]
gend follow n n_se percentage percentage_low percentage_upp
<dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 0 1 8.23 3.74 1.43 0.590 3.45
2 0 2 57.7 9.55 10.1 7.36 13.6
3 0 3 112. 11.2 19.6 16.2 23.5
4 0 4 282. 18.3 49.2 44.5 53.9
5 0 5 113. 10.4 19.7 16.5 23.4
6 1 1 14.7 3.84 2.70 1.62 4.48
7 1 2 56.3 9.54 10.3 7.48 14.1
8 1 3 100. 11.4 18.4 14.9 22.5
9 1 4 233. 15.3 42.7 37.9 47.7
10 1 5 141. 15.9 25.9 21.3 31.0 Two Categorical Variables
Using the survey package:
Two Categorical Variables
follow
gend 1 2 3 4 5
0 1.434811 10.066314 19.595173 49.195379 19.708324
1 2.701539 10.314729 18.382247 42.731941 25.869544Visualizing Cross-Tabulations
Grouped bar chart with confidence intervals:
survey_tidy |>
drop_na(gend, follow) |>
group_by(gend, follow) |>
summarise(percentage = survey_prop(vartype = "ci") * 100) |>
ggplot(aes(x = follow, y = percentage, fill = factor(gend))) +
geom_col(position = "dodge") +
geom_errorbar(aes(ymin = percentage_low, ymax = percentage_upp),
width = 0.2,
position = position_dodge(width = 0.9)) +
scale_x_continuous(breaks = 1:5,
labels = c("Strongly disagree",
"Disagree",
"Neither disagree\nnor agree",
"Agree",
"Strongly agree")) +
scale_fill_discrete(breaks = c("0", "1"),
labels = c("Female", "Male")) +
labs(title = "How much do you agree or disagree with the following statements?",
subtitle = "I follow the weather very closely.",
x = "Response Category",
y = "Estimated Percent of Respondents (95% CI)",
fill = "Gender") +
theme_classic()Visualizing Cross-Tabulations
Statistical Tests
T-Test
Compare means of a continuous variable across two groups:
T-Test
Welch Two Sample t-test
data: follow by gend
t = -0.069959, df = 1348, p-value = 0.9442
alternative hypothesis: true difference in means between group 0 and group 1 is not equal to 0
95 percent confidence interval:
-0.1053141 0.0980614
sample estimates:
mean in group 0 mean in group 1
3.789231 3.792857 T-Test
With weights (using survey package):
T-Test
Design-based t-test
data: follow ~ gend
t = 0.43074, df = 1348, p-value = 0.6667
alternative hypothesis: true difference in mean is not equal to 0
95 percent confidence interval:
-0.1093706 0.1709132
sample estimates:
difference in mean
0.03077132 Regression Analysis
Linear Regression
For continuous outcome variables:
Linear Regression
Call:
lm(formula = follow ~ gend + risk_tor, data = survey_raw)
Residuals:
Min 1Q Median 3Q Max
-3.1486 -0.6941 0.1554 0.4563 1.4564
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 3.393163 0.068437 49.581 < 2e-16 ***
gend 0.003062 0.051103 0.060 0.952
risk_tor 0.150484 0.021783 6.908 7.55e-12 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.9374 on 1345 degrees of freedom
(6 observations deleted due to missingness)
Multiple R-squared: 0.03427, Adjusted R-squared: 0.03283
F-statistic: 23.86 on 2 and 1345 DF, p-value: 6.553e-11Linear Regression
With weights (using survey package):
Linear Regression
Call:
svyglm(formula = follow ~ gend + risk_tor, design = survey_base,
family = gaussian())
Survey design:
svydesign(ids = ~1, weights = ~weightfactor, data = survey_raw)
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 3.29590 0.09739 33.841 < 2e-16 ***
gend 0.03413 0.06915 0.494 0.622
risk_tor 0.17648 0.03134 5.632 2.17e-08 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for gaussian family taken to be 0.9154299)
Number of Fisher Scoring iterations: 2Linear Regression
Get confidence intervals:
Linear Regression
2.5 % 97.5 %
(Intercept) 3.1048377 3.4869555
gend -0.1015246 0.1697821
risk_tor 0.1150052 0.2379484Logistic Regression
For binary outcome variables:
Logistic Regression
Call:
glm(formula = exf_aware == 1 ~ gend + risk_tor, family = binomial(link = "logit"),
data = survey_raw)
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -0.2753 0.1558 -1.767 0.07726 .
gend 0.3720 0.1169 3.182 0.00146 **
risk_tor 0.2919 0.0521 5.601 2.13e-08 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 1727.8 on 1347 degrees of freedom
Residual deviance: 1685.1 on 1345 degrees of freedom
(6 observations deleted due to missingness)
AIC: 1691.1
Number of Fisher Scoring iterations: 4Logistic Regression
With weights (using survey package):
Logistic Regression
Call:
svyglm(formula = exf_aware == 1 ~ gend + risk_tor, design = survey_base,
family = binomial(link = "logit"))
Survey design:
svydesign(ids = ~1, weights = ~weightfactor, data = survey_raw)
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.46974 0.20429 -2.299 0.0216 *
gend 0.37227 0.15017 2.479 0.0133 *
risk_tor 0.37056 0.07034 5.268 1.6e-07 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for binomial family taken to be 1.004359)
Number of Fisher Scoring iterations: 4Rake Weighting
Calculate Rake Weights
Rake weighting adjusts survey data to match known population distributions:
- Purpose: Post-stratification adjustment to match population margins
- Targets: Known population proportions (from ACS, CPS, etc.)
- Method: Iterative proportional fitting (raking)
Define Population Targets
Convert to Frequency Targets
# Convert proportions to counts based on your sample size
sample_size <- nrow(survey_raw)
age_targets <- pop_age |>
mutate(Freq = round(prop * sample_size)) |>
select(age_group, Freq)
age_targets
gender_targets <- pop_gender |>
mutate(Freq = round(prop * sample_size)) |>
select(gend_group, Freq)
gender_targetsConvert to Frequency Targets
# A tibble: 3 × 2
age_group Freq
<chr> <dbl>
1 18-34 420
2 35-54 487
3 55+ 447# A tibble: 2 × 2
gend_group Freq
<chr> <dbl>
1 Female 691
2 Male 663Add Demographic Grouping Variables
Create Unweighted Design and Rake
# Create unweighted survey design
survey_raw_unwtd <- svydesign(ids = ~1, weights = ~1, data = survey_raw)
# Rake sample to match population margins
survey_raw_raked <- rake(
design = survey_raw_unwtd,
sample.margins = list(~age_group, ~gend_group),
population.margins = list(age_targets, gender_targets),
control = list(maxit = 50, epsilon = 1e-7, verbose = FALSE)
)Check Weight Distribution
Check Weight Distribution
[1] 0.7088422[1] 1.62208[1] 1[1] 1354Add Weights Back to Dataset
Survey Data Analysis (Quantitative)